Quality of Life in Chronic Hemodialysis Patients
Bibliographic record
Abstract
Purpose: Quality of life (QoL) is a well‐recognized important measure of therapy outcome, as it reflects what patients perceive as their health condition. The aim of this study was to estimate the QoL in patients on HD and to find the factors that mainly affect it. Patients and Methods: We studied 70 patients on HD (38 male, age 57.86 ± 14.63 years) with the use of kidney disease quality of life short form. Physical health (PH), mental health (MH), kidney disease issues (KDIs), and patient satisfaction (PS) were assessed, as well as Khan comorbidity index, adequacy of dialysis, nutrition, and epidemiologic and laboratory data. Results: PH was significantly correlated with comorbidity (p < 0001), age (p < 0001), duration of HD (p < 0001), serum albumin (Salb) (p < 0005), the existence of a living relative donor (p < 0001), Hb (p < 0.01), and CRP (p < 0.01). MH was significantly correlated to comorbidity (p < 0001), age (p < 0001), duration of HD (p < 0001), Salb (p = 0002), the existence of a living relative donor (p < 0001) and Hb (p < 0.01). KDI score was significantly correlated with comorbidity (p < 0001), age (p < 0001), duration of HD (p < 0001), and Hb (p < 0.05). The acceptance of the method was significantly lower in patients with AVF dysfunction (p < 0005). As much as 44.3% of patients presented inadequate compliance to dietary and fluid restrictions. Conclusion: Frequent QoL assessment in patients on HD is a useful tool for professionals involved in patients' care. Older age, long time on HD, malnutrition, elevated CRP, and comorbid conditions are correlated to lower QoL scores.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".